Papers by Dohyung Kim
Can LLMs Recognize Toxicity? A Structured Investigation Framework and Toxicity Metric (2024.findings-emnlp)
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| Challenge: | Existing toxicity metrics rely on encoder models trained on specific toxicity datasets, which are susceptible to out-of-distribution (OOD) problems and depend on the dataset’s definition of toxicity. |
| Approach: | They propose a robust metric grounded on LLMs to flexibly measure toxicity according to the given definition by analysing toxicity factors and intrinsic toxic attributes. |
| Outcome: | The proposed metric improves on conventional metrics by 12 points in the F1 score and shows that upstream toxicity significantly influences downstream metrics, suggesting that LLMs are unsuitable for toxicity evaluations within unverified factors. |
KoBEST: Korean Balanced Evaluation of Significant Tasks (2022.coling-1)
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| Challenge: | a well-formulated benchmark allows objective and precise evaluation of diverse models. |
| Approach: | They propose a benchmark for Korean balanced evaluation of significant tasks that requires advanced Korean linguistic knowledge. |
| Outcome: | The proposed benchmarks are based on five Korean-language downstream tasks . the data is annotated by humans and thoroughly reviewed to guarantee high data quality. |
ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State Reflection (2025.emnlp-main)
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| Challenge: | Recent advances in LLMs have significantly enhanced their reasoning capabilities, enabling LLM-based agents to perform complex multi-step decision making beyond static problem solving. |
| Approach: | They propose a novel reasoning backbone that shifts reasoning from merely planning next actions to continuously reflecting on the agent’s state relative to its goal. |
| Outcome: | The proposed model outperforms ReAct by 27.7% on average, achieving a 93.3% success rate in ALFWorld. |
Conditional [MASK] Discrete Diffusion Language Model (2025.emnlp-main)
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| Challenge: | Auto-regressive models excel in natural language processing but struggle to generate diverse text and lack controllability. |
| Approach: | They propose entropy-adaptive Gibbs sampling and entropic-based noise scheduling to counterbalance each model’s shortcomings. |
| Outcome: | The proposed framework outperforms baseline models and achieves the best quality-diversity tradeoff, demonstrating its effectiveness in non-autoregressive text generation. |